Overview

Dataset statistics

Number of variables10
Number of observations2935849
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)< 0.1%
Total size in memory224.0 MiB
Average record size in memory80.0 B

Variable types

DateTime1
Numeric6
Text3

Alerts

Dataset has 6 (< 0.1%) duplicate rowsDuplicates
item_cnt_day is highly skewed (γ1 = 272.8331617)Skewed
date_block_num has 115690 (3.9%) zerosZeros

Reproduction

Analysis started2024-04-08 13:35:38.398275
Analysis finished2024-04-08 13:36:12.449031
Duration34.05 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

date
Date

Distinct1034
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
Minimum2013-01-01 00:00:00
Maximum2015-12-10 00:00:00
2024-04-08T22:36:12.530427image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:12.655456image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

date_block_num
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.569911
Minimum0
Maximum33
Zeros115690
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:12.775841image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median14
Q323
95-th percentile31
Maximum33
Range33
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.4229877
Coefficient of variation (CV)0.64674296
Kurtosis-1.082869
Mean14.569911
Median Absolute Deviation (MAD)8
Skewness0.20385795
Sum42775060
Variance88.792697
MonotonicityIncreasing
2024-04-08T22:36:12.892144image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
11 143246
 
4.9%
23 130786
 
4.5%
2 121347
 
4.1%
0 115690
 
3.9%
1 108613
 
3.7%
7 104772
 
3.6%
6 100548
 
3.4%
5 100403
 
3.4%
12 99349
 
3.4%
10 96736
 
3.3%
Other values (24) 1814359
61.8%
ValueCountFrequency (%)
0 115690
3.9%
1 108613
3.7%
2 121347
4.1%
3 94109
3.2%
4 91759
3.1%
5 100403
3.4%
6 100548
3.4%
7 104772
3.6%
8 96137
3.3%
9 94202
3.2%
ValueCountFrequency (%)
33 53514
1.8%
32 50588
1.7%
31 57029
1.9%
30 55549
1.9%
29 54617
1.9%
28 54548
1.9%
27 56274
1.9%
26 69977
2.4%
25 71808
2.4%
24 88522
3.0%

shop_id
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.001728
Minimum0
Maximum59
Zeros9857
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:13.016218image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q122
median31
Q347
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)25

Descriptive statistics

Standard deviation16.226973
Coefficient of variation (CV)0.4917007
Kurtosis-1.0253581
Mean33.001728
Median Absolute Deviation (MAD)13
Skewness-0.072361429
Sum96888091
Variance263.31465
MonotonicityNot monotonic
2024-04-08T22:36:13.143251image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 235636
 
8.0%
25 186104
 
6.3%
54 143480
 
4.9%
28 142234
 
4.8%
57 117428
 
4.0%
42 109253
 
3.7%
27 105366
 
3.6%
6 82663
 
2.8%
58 71441
 
2.4%
56 69573
 
2.4%
Other values (50) 1672671
57.0%
ValueCountFrequency (%)
0 9857
 
0.3%
1 5678
 
0.2%
2 25991
 
0.9%
3 25532
 
0.9%
4 38242
1.3%
5 38179
1.3%
6 82663
2.8%
7 58076
2.0%
8 3412
 
0.1%
9 3751
 
0.1%
ValueCountFrequency (%)
59 42108
 
1.4%
58 71441
2.4%
57 117428
4.0%
56 69573
2.4%
55 34769
 
1.2%
54 143480
4.9%
53 52921
 
1.8%
52 43502
 
1.5%
51 44433
 
1.5%
50 65173
2.2%

item_id
Real number (ℝ)

Distinct21807
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10197.227
Minimum0
Maximum22169
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:13.273341image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1540
Q14476
median9343
Q315684
95-th percentile20949
Maximum22169
Range22169
Interquartile range (IQR)11208

Descriptive statistics

Standard deviation6324.2974
Coefficient of variation (CV)0.62019776
Kurtosis-1.22521
Mean10197.227
Median Absolute Deviation (MAD)5492
Skewness0.25717355
Sum2.9937519 × 1010
Variance39996737
MonotonicityNot monotonic
2024-04-08T22:36:13.407582image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20949 31340
 
1.1%
5822 9408
 
0.3%
17717 9067
 
0.3%
2808 7479
 
0.3%
4181 6853
 
0.2%
7856 6602
 
0.2%
3732 6475
 
0.2%
2308 6320
 
0.2%
4870 5811
 
0.2%
3734 5805
 
0.2%
Other values (21797) 2840689
96.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 6
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
22169 1
 
< 0.1%
22168 6
 
< 0.1%
22167 1114
< 0.1%
22166 270
 
< 0.1%
22165 2
 
< 0.1%
22164 408
 
< 0.1%
22163 71
 
< 0.1%
22162 560
< 0.1%
22161 1
 
< 0.1%
22160 49
 
< 0.1%

item_price
Real number (ℝ)

Distinct19993
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean890.85323
Minimum-1
Maximum307980
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size22.4 MiB
2024-04-08T22:36:13.543176image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q1249
median399
Q3999
95-th percentile2690
Maximum307980
Range307981
Interquartile range (IQR)750

Descriptive statistics

Standard deviation1729.7996
Coefficient of variation (CV)1.9417336
Kurtosis445.53283
Mean890.85323
Median Absolute Deviation (MAD)250
Skewness10.750423
Sum2.6154106 × 109
Variance2992206.8
MonotonicityNot monotonic
2024-04-08T22:36:13.675148image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299 291352
 
9.9%
399 242603
 
8.3%
149 218432
 
7.4%
199 184044
 
6.3%
349 101461
 
3.5%
599 95673
 
3.3%
999 82784
 
2.8%
799 77882
 
2.7%
249 77685
 
2.6%
699 76493
 
2.6%
Other values (19983) 1487440
50.7%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0.07 2
 
< 0.1%
0.0875 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2932
0.1%
0.2 1
 
< 0.1%
0.5 1226
< 0.1%
0.9087136929 1
 
< 0.1%
0.99 493
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
307980 1
 
< 0.1%
59200 1
 
< 0.1%
50999 1
 
< 0.1%
49782 1
 
< 0.1%
42990 4
< 0.1%
42000 1
 
< 0.1%
41990 3
< 0.1%
40991 1
 
< 0.1%
40900 1
 
< 0.1%
37991 2
< 0.1%

item_cnt_day
Real number (ℝ)

SKEWED 

Distinct198
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2426409
Minimum-22
Maximum2169
Zeros0
Zeros (%)0.0%
Negative7356
Negative (%)0.3%
Memory size22.4 MiB
2024-04-08T22:36:13.872267image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum-22
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum2169
Range2191
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6188344
Coefficient of variation (CV)2.1074749
Kurtosis177478.1
Mean1.2426409
Median Absolute Deviation (MAD)0
Skewness272.83316
Sum3648206
Variance6.8582938
MonotonicityNot monotonic
2024-04-08T22:36:14.013971image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2629372
89.6%
2 194201
 
6.6%
3 47350
 
1.6%
4 19685
 
0.7%
5 10474
 
0.4%
-1 7252
 
0.2%
6 6338
 
0.2%
7 4057
 
0.1%
8 2903
 
0.1%
9 2177
 
0.1%
Other values (188) 12040
 
0.4%
ValueCountFrequency (%)
-22 1
 
< 0.1%
-16 1
 
< 0.1%
-9 1
 
< 0.1%
-6 2
 
< 0.1%
-5 4
 
< 0.1%
-4 3
 
< 0.1%
-3 14
 
< 0.1%
-2 78
 
< 0.1%
-1 7252
 
0.2%
1 2629372
89.6%
ValueCountFrequency (%)
2169 1
< 0.1%
1000 1
< 0.1%
669 1
< 0.1%
637 1
< 0.1%
624 1
< 0.1%
539 1
< 0.1%
533 1
< 0.1%
512 1
< 0.1%
508 1
< 0.1%
504 1
< 0.1%
Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:14.194661image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length47
Median length31
Mean length22.517262
Min length14

Characters and Unicode

Total characters66107281
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowЯрославль ТЦ "Альтаир"
2nd rowМосква ТРК "Атриум"
3rd rowМосква ТРК "Атриум"
4th rowМосква ТРК "Атриум"
5th rowМосква ТРК "Атриум"
ValueCountFrequency (%)
тц 1651630
 
15.9%
москва 996636
 
9.6%
мега 544689
 
5.3%
трц 387410
 
3.7%
ii 284579
 
2.7%
тк 252032
 
2.4%
семеновский 235636
 
2.3%
трк 234360
 
2.3%
якутск 204404
 
2.0%
атриум 186104
 
1.8%
Other values (101) 5383085
52.0%
2024-04-08T22:36:14.505469image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7424716
 
11.2%
" 5170714
 
7.8%
а 4306636
 
6.5%
о 3616165
 
5.5%
к 3128346
 
4.7%
е 2993274
 
4.5%
Т 2876617
 
4.4%
с 2691262
 
4.1%
в 2419562
 
3.7%
Ц 2361816
 
3.6%
Other values (63) 29118173
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66107281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7424716
 
11.2%
" 5170714
 
7.8%
а 4306636
 
6.5%
о 3616165
 
5.5%
к 3128346
 
4.7%
е 2993274
 
4.5%
Т 2876617
 
4.4%
с 2691262
 
4.1%
в 2419562
 
3.7%
Ц 2361816
 
3.6%
Other values (63) 29118173
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66107281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7424716
 
11.2%
" 5170714
 
7.8%
а 4306636
 
6.5%
о 3616165
 
5.5%
к 3128346
 
4.7%
е 2993274
 
4.5%
Т 2876617
 
4.4%
с 2691262
 
4.1%
в 2419562
 
3.7%
Ц 2361816
 
3.6%
Other values (63) 29118173
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66107281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7424716
 
11.2%
" 5170714
 
7.8%
а 4306636
 
6.5%
о 3616165
 
5.5%
к 3128346
 
4.7%
е 2993274
 
4.5%
Т 2876617
 
4.4%
с 2691262
 
4.1%
в 2419562
 
3.7%
Ц 2361816
 
3.6%
Other values (63) 29118173
44.0%
Distinct21807
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:14.765265image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length150
Median length104
Mean length42.176862
Min length2

Characters and Unicode

Total characters123824899
Distinct characters165
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2371 ?
Unique (%)0.1%

Sample

1st rowЯВЛЕНИЕ 2012 (BD)
2nd rowDEEP PURPLE The House Of Blue Light LP
3rd rowDEEP PURPLE The House Of Blue Light LP
4th rowDEEP PURPLE Who Do You Think We Are LP
5th rowDEEP PURPLE 30 Very Best Of 2CD (Фирм.)
ValueCountFrequency (%)
версия 725653
 
4.0%
русская 680070
 
3.7%
pc 445501
 
2.4%
jewel 316847
 
1.7%
ps3 230252
 
1.3%
bd 216320
 
1.2%
3 210744
 
1.2%
регион 199715
 
1.1%
xbox 188717
 
1.0%
2 175655
 
1.0%
Other values (19552) 14926755
81.5%
2024-04-08T22:36:15.179946image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16075953
 
13.0%
р 3867036
 
3.1%
с 3693825
 
3.0%
и 3603497
 
2.9%
а 3465014
 
2.8%
е 3443034
 
2.8%
e 3295768
 
2.7%
о 2550540
 
2.1%
к 2320910
 
1.9%
я 2287092
 
1.8%
Other values (155) 79222230
64.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123824899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16075953
 
13.0%
р 3867036
 
3.1%
с 3693825
 
3.0%
и 3603497
 
2.9%
а 3465014
 
2.8%
е 3443034
 
2.8%
e 3295768
 
2.7%
о 2550540
 
2.1%
к 2320910
 
1.9%
я 2287092
 
1.8%
Other values (155) 79222230
64.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123824899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16075953
 
13.0%
р 3867036
 
3.1%
с 3693825
 
3.0%
и 3603497
 
2.9%
а 3465014
 
2.8%
е 3443034
 
2.8%
e 3295768
 
2.7%
о 2550540
 
2.1%
к 2320910
 
1.9%
я 2287092
 
1.8%
Other values (155) 79222230
64.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123824899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16075953
 
13.0%
р 3867036
 
3.1%
с 3693825
 
3.0%
и 3603497
 
2.9%
а 3465014
 
2.8%
е 3443034
 
2.8%
e 3295768
 
2.7%
о 2550540
 
2.1%
к 2320910
 
1.9%
я 2287092
 
1.8%
Other values (155) 79222230
64.0%

item_category_id
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.001383
Minimum0
Maximum83
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:15.311230image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q128
median40
Q355
95-th percentile71
Maximum83
Range83
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.100759
Coefficient of variation (CV)0.42750418
Kurtosis-0.52515786
Mean40.001383
Median Absolute Deviation (MAD)15
Skewness0.31828252
Sum1.1743802 × 108
Variance292.43594
MonotonicityNot monotonic
2024-04-08T22:36:15.437632image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 564652
19.2%
30 351591
12.0%
55 339585
11.6%
19 208219
 
7.1%
37 192674
 
6.6%
23 146789
 
5.0%
28 121539
 
4.1%
20 79058
 
2.7%
63 53845
 
1.8%
65 53227
 
1.8%
Other values (74) 824670
28.1%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 2
 
< 0.1%
2 18461
0.6%
3 25283
0.9%
4 2304
 
0.1%
5 7231
 
0.2%
6 18498
0.6%
7 4459
 
0.2%
8 1877
 
0.1%
9 2193
 
0.1%
ValueCountFrequency (%)
83 7206
 
0.2%
82 4390
 
0.1%
81 795
 
< 0.1%
80 1325
 
< 0.1%
79 9067
 
0.3%
78 2346
 
0.1%
77 3703
 
0.1%
76 3746
 
0.1%
75 42603
1.5%
74 56
 
< 0.1%
Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.4 MiB
2024-04-08T22:36:15.601704image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Length

Max length40
Median length36
Mean length20.538608
Min length9

Characters and Unicode

Total characters60298252
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowКино - Blu-Ray
2nd rowМузыка - Винил
3rd rowМузыка - Винил
4th rowМузыка - Винил
5th rowМузыка - CD фирменного производства
ValueCountFrequency (%)
2905446
25.4%
игры 1125431
 
9.8%
кино 838291
 
7.3%
dvd 564652
 
4.9%
pc 506039
 
4.4%
издания 486912
 
4.3%
музыка 406737
 
3.6%
подарки 370450
 
3.2%
стандартные 351591
 
3.1%
cd 347516
 
3.0%
Other values (90) 3551360
31.0%
2024-04-08T22:36:15.899711image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8518576
 
14.1%
и 3952343
 
6.6%
о 3949536
 
6.6%
а 3372489
 
5.6%
н 3154622
 
5.2%
- 3141462
 
5.2%
р 2865626
 
4.8%
ы 2672140
 
4.4%
г 1843617
 
3.1%
д 1758242
 
2.9%
Other values (83) 25069599
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60298252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8518576
 
14.1%
и 3952343
 
6.6%
о 3949536
 
6.6%
а 3372489
 
5.6%
н 3154622
 
5.2%
- 3141462
 
5.2%
р 2865626
 
4.8%
ы 2672140
 
4.4%
г 1843617
 
3.1%
д 1758242
 
2.9%
Other values (83) 25069599
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60298252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8518576
 
14.1%
и 3952343
 
6.6%
о 3949536
 
6.6%
а 3372489
 
5.6%
н 3154622
 
5.2%
- 3141462
 
5.2%
р 2865626
 
4.8%
ы 2672140
 
4.4%
г 1843617
 
3.1%
д 1758242
 
2.9%
Other values (83) 25069599
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60298252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8518576
 
14.1%
и 3952343
 
6.6%
о 3949536
 
6.6%
а 3372489
 
5.6%
н 3154622
 
5.2%
- 3141462
 
5.2%
р 2865626
 
4.8%
ы 2672140
 
4.4%
г 1843617
 
3.1%
д 1758242
 
2.9%
Other values (83) 25069599
41.6%

Interactions

2024-04-08T22:36:06.751023image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:35:59.100700image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:00.694726image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:02.219417image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:03.752430image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:05.250922image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:07.014912image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:35:59.358547image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:00.940313image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:02.499869image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:03.999543image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:05.513439image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:07.266433image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:35:59.696334image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:01.190280image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:02.768936image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:04.259136image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:05.765272image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:07.508376image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:35:59.943413image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:01.443949image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:03.014132image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:04.492894image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:06.011220image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:07.819865image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:00.192033image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:01.703730image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:03.263855image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:04.750033image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:06.256212image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:08.066541image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:00.445895image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:01.965378image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:03.510098image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:05.001728image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
2024-04-08T22:36:06.497463image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/

Correlations

2024-04-08T22:36:15.984266image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
date_block_numitem_category_iditem_cnt_dayitem_iditem_priceshop_id
date_block_num1.0000.0130.0030.0090.1370.022
item_category_id0.0131.000-0.0150.414-0.4050.028
item_cnt_day0.003-0.0151.000-0.0040.046-0.002
item_id0.0090.414-0.0041.000-0.3240.031
item_price0.137-0.4050.046-0.3241.000-0.051
shop_id0.0220.028-0.0020.031-0.0511.000

Missing values

2024-04-08T22:36:08.393521image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-08T22:36:09.529322image/svg+xmlMatplotlib v3.8.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datedate_block_numshop_iditem_iditem_priceitem_cnt_dayshop_nameitem_nameitem_category_iditem_category_name
002.01.201305922154999.001.0Ярославль ТЦ "Альтаир"ЯВЛЕНИЕ 2012 (BD)37Кино - Blu-Ray
103.01.20130252552899.001.0Москва ТРК "Атриум"DEEP PURPLE The House Of Blue Light LP58Музыка - Винил
205.01.20130252552899.00-1.0Москва ТРК "Атриум"DEEP PURPLE The House Of Blue Light LP58Музыка - Винил
306.01.201302525541709.051.0Москва ТРК "Атриум"DEEP PURPLE Who Do You Think We Are LP58Музыка - Винил
415.01.201302525551099.001.0Москва ТРК "Атриум"DEEP PURPLE 30 Very Best Of 2CD (Фирм.)56Музыка - CD фирменного производства
510.01.20130252564349.001.0Москва ТРК "Атриум"DEEP PURPLE Perihelion: Live In Concert DVD (Кир.)59Музыка - Музыкальное видео
602.01.20130252565549.001.0Москва ТРК "Атриум"DEEP PURPLE Stormbringer (фирм.)56Музыка - CD фирменного производства
704.01.20130252572239.001.0Москва ТРК "Атриум"DEFTONES Koi No Yokan55Музыка - CD локального производства
811.01.20130252572299.001.0Москва ТРК "Атриум"DEFTONES Koi No Yokan55Музыка - CD локального производства
903.01.20130252573299.003.0Москва ТРК "Атриум"DEL REY LANA Born To Die55Музыка - CD локального производства
datedate_block_numshop_iditem_iditem_priceitem_cnt_dayshop_nameitem_nameitem_category_iditem_category_name
293583924.10.201533257315399.01.0Москва ТРК "Атриум"V/A Dance Kick! 2CD (digipack)55Музыка - CD локального производства
293584031.10.201533257409299.01.0Москва ТРК "Атриум"V/A Nu Jazz Selection (digipack)55Музыка - CD локального производства
293584111.10.201533257393349.01.0Москва ТРК "Атриум"V/A Lounge Del Mar 3 2CD (digipack)55Музыка - CD локального производства
293584210.10.201533257384749.01.0Москва ТРК "Атриум"V/A Ladies Sing The Blues 3CD55Музыка - CD локального производства
293584309.10.201533257409299.01.0Москва ТРК "Атриум"V/A Nu Jazz Selection (digipack)55Музыка - CD локального производства
293584410.10.201533257409299.01.0Москва ТРК "Атриум"V/A Nu Jazz Selection (digipack)55Музыка - CD локального производства
293584509.10.201533257460299.01.0Москва ТРК "Атриум"V/A The Golden Jazz Collection 1 2CD55Музыка - CD локального производства
293584614.10.201533257459349.01.0Москва ТРК "Атриум"V/A The Best Of The 3 Tenors55Музыка - CD локального производства
293584722.10.201533257440299.01.0Москва ТРК "Атриум"V/A Relax Collection Planet MP3 (mp3-CD) (jewel)57Музыка - MP3
293584803.10.201533257460299.01.0Москва ТРК "Атриум"V/A The Golden Jazz Collection 1 2CD55Музыка - CD локального производства

Duplicate rows

Most frequently occurring

datedate_block_numshop_iditem_iditem_priceitem_cnt_dayshop_nameitem_nameitem_category_iditem_category_name# duplicates
001.05.201416503423999.01.0Тюмень ТЦ "Гудвин"Far Cry 3 (Classics) [Xbox 360, русская версия]23Игры - XBOX 3602
105.01.201305420130149.01.0Химки ТЦ "Мега"УЧЕНИК ЧАРОДЕЯ (регион)40Кино - DVD2
212.07.201418253423999.01.0Москва ТРК "Атриум"Far Cry 3 (Classics) [Xbox 360, русская версия]23Игры - XBOX 3602
323.02.201413503423999.01.0Тюмень ТЦ "Гудвин"Far Cry 3 (Classics) [Xbox 360, русская версия]23Игры - XBOX 3602
423.03.201414213423999.01.0Москва МТРЦ "Афи Молл"Far Cry 3 (Classics) [Xbox 360, русская версия]23Игры - XBOX 3602
531.12.2014234221619499.01.0СПб ТК "Невский Центр"ЧЕЛОВЕК ДОЖДЯ (BD)37Кино - Blu-Ray2